Ground-level ozone (O3) pollution has been steadily
getting worse in most parts of eastern China during the past 5 years. The
non-linearity of O3 formation with its precursors like nitrogen oxides
(NOx= NO + NO2) and volatile organic compounds (VOCs) are
complicating effective O3 abatement plans. The diagnosis from
space-based observations, i.e. the ratio of formaldehyde (HCHO) columns to
tropospheric NO2 columns (HCHO / NO2), has previously been proved to
be highly consistent with our current understanding of surface O3
chemistry. HCHO / NO2 ratio thresholds distinguishing O3 formation
sensitivity depend on regions and O3 chemistry interactions with
aerosol. To shed more light on the current O3 formation sensitivity
over China, we have derived HCHO / NO2 ratio thresholds by directly
connecting satellite-based HCHO / NO2 observations and ground-based
O3 measurements over the major Chinese cities in this study. We find
that a VOC-limited regime occurs for HCHO / NO2< 2.3, and
a NOx-limited regime occurs for HCHO / NO2> 4.2. The
HCHO / NO2 between 2.3 and 4.2 reflects the transition between the two
regimes. Our method shows that the O3 formation sensitivity tends to be
VOC-limited over urban areas and NOx-limited over rural and remote
areas in China. We find that there is a shift in some cities from the
VOC-limited regime to the transitional regime that is associated with a rapid drop
in anthropogenic NOx emissions, owing to the widely applied rigorous
emission control strategies between 2016 and 2019. This detected spatial
expansion of the transitional regime is supported by rising surface O3
concentrations. The enhanced O3 concentrations in urban areas during
the COVID-19 lockdown in China indicate that a protocol with simultaneous
anthropogenic NOx emissions and VOC emissions controls is essential for
O3 abatement plans.
Introduction
Ground-level ozone (O3) is one of the major air pollutants that has
negative impacts on human health and can result in eye and nose irritation,
respiratory disease, and lung function impairment (Jerrett et al., 2009;
Khaniabadi et al., 2017; Huang et al., 2018). Y. Tian et al. (2020)
observed increased admissions for pneumonia associated with O3
exposure, especially for elderly people. In addition, it also has important
impacts on climate as a greenhouse gas by absorbing thermal radiation
(Fishman et al., 1979; IPCC, 2014). Photochemical tropospheric O3 is formed in a non-linear manner from O3 precursors such as volatile
organic compounds (VOCs) and nitrogen oxides (NOx= NO + NO2)
in the presence of sunlight (Crutzen, 1974; Jacob, 2000).
In 2008, China was found to be the largest contributor to Asian emissions of
carbon monoxide (CO), NOx, non-methane volatile organic carbon (NMVOC),
and methane (CH4) (Kurokawa et al., 2013).
Because of these large emissions of anthropogenic air pollutants, the
Chinese State Council released the “Air Pollution Prevention and Action
Plan” (APPAP) on September 2013, which has as a key task to prevent and
control air pollution in China (Cai et al., 2017). Since then,
critical emission control strategies have been carried out that are designed
to reduce the concentrations of six environmental pollutants: sulfur dioxide
(SO2), nitrogen dioxide (NO2), CO, O3, and particulate matter
(PM2.5 and PM10) (Zhang et al., 2016; Feng and Liao, 2016).
During the past decade, the concentrations of many pollutants including
SO2, NO2, CO, PM2.5, and PM10 have declined in most
cities; however, O3 concentrations showed an increasing trend (W. N. Wang
et al., 2017; Z. Wang et al., 2019; Zeng et al., 2019). Therefore, reducing
O3 concentrations has become the focus of China's next air quality
control strategy (Cheng et al., 2018).
In terms of O3 concentrations, the effectiveness of emissions control
strategy depends on whether the photochemical regime of O3 formation is
a VOC-limited or NOx-limited regime (Jin et al., 2020). In the
VOC-limited (or NOx-saturated) regime, VOC emission reductions reduce
the chemical production of organic radicals (RO2), which in turn lead
to decreased cycling with NOx and consequently lower concentration of
O3 (Milford et al., 1989). In the NOx-limited (or VOC-saturated)
regime, NOx emission reductions reduce NO2 photolysis, which is
the primary source of free oxygen atoms. Therefore, in a NOx-limited
regime, NOx reductions reduce ambient O3. In contrast, in
a VOC-limited regime, NOx acts to reduce O3, so a NOx decrease
in emissions promotes O3 production (Kleinman, 1994).
The observed photochemical indicators and observation-based models (OBMs) are
the most commonly used tools to diagnose the O3 formation sensitivity.
O3 production efficiency (OPE =ΔO3/ΔNOz) and
the H2O2/ NOz (or H2O2/ HNO3) ratio are two
widely used indicators to infer the O3 formation regimes (Chou et
al., 2011; Ding et al., 2013). T. Wang et al. (2017) concluded
that lower OPE values (e.g. < 4) indicate a VOC-limited regime. In
contrast, higher OPE values (e.g. > 7) indicate a
NOx-limited regime. OPE values in the medium range (e.g. 4 < OPE < 7) mark the transition between the two regimes. Another indicator
of the O3 formation sensitivity regime is the H2O2/ NOz
ratio. Hammer et al. (2002) defined that, in the VOC-limited
regime, lower H2O2/ NOz ratios would be expected and higher
H2O2/ NOz ratios indicate the NOx-limited regime. In the
past decade, the observed photochemical indicators have been applied to
identify the O3 formation sensitivity in different periods and regions
of China.
The OBM combines in situ field observations and chemical box modelling. It is built
on widely used chemistry mechanisms (e.g. Master Chemical Mechanism (MCM), Carbon Bond,
Regional Atmospheric Chemical Mechanism (RACM), Statewide Air Pollution Research Center mechanism (SAPRC))
and applied to the observed atmospheric conditions to simulate various
atmospheric chemical processes, including the in situ O3 production rate.
However, ground-based measurements are often limited in time period and
spatial extent. The OBM analysis requires measuring nitric oxide (NO) at
sub-ppb levels and more than 50 different types of VOCs with high
accuracy, which is difficult to achieve (T. Wang et al., 2017).
Satellite remote sensing provides an alternative way to investigate long
time periods of O3 formation sensitivity on large spatial scales. For
over 2 decades, satellite-based spectrometers have provided continuous
global observations on a daily basis for two species indicative of O3
precursors, i.e. NO2 for NOx (Martin et al., 2004; Lamsal et al.,
2014) and formaldehyde (HCHO) for VOCs (Palmer et al., 2003; Fu et al.,
2007). NOx can be approximated from satellite observation of NO2
column because of the short lifetime of NOx and high ratio of
NO2/ NOx in the boundary layer (Duncan et al., 2010; Jin and
Holloway, 2015). HCHO is an intermediate of the oxidation reaction of
various VOCs in the atmosphere. The production of HCHO is approximately
proportional to the summed rate of reactions of VOC with OH radicals
(Sillman, 1995). Therefore, HCHO can be used as a tracer for VOCs in the
absence of other VOC observations (Martin et al., 2004; Duncan et al.,
2010). The O3 formation sensitivity is defined by the ratio of HCHO to
NO2 (referred to as FNR) (Martin et al., 2004). Duncan
et al. (2010) combined models and
Ozone Monitoring Instrument (OMI) HCHO and NO2 data to show certain
ranges of FNR that can be useful for classifying a region into VOC-limited
or NOx-limited regime. An FNR smaller than 1 indicates the VOC-limited
conditions, and an FNR higher than 2 indicates the NOx-limited
conditions. An FNR in the range of 1–2 should generally be considered
indicative of the transitional regime. These FNR thresholds defined by
Duncan et al. (2010) have been widely used for various regions (Choi
and Souri, 2015; Jin and Holloway, 2015; Souri et al., 2017; Jeon et al.,
2018) and with different satellite instruments (Choi et
al., 2012).
However, these prior studies linked FNR with surface O3 sensitivity in
models (Martin et al., 2004; Duncan et al., 2010). Modelled and
observed HCHO columns, NO2 columns, and surface O3 often disagree.
Jin et al. (2017) found that the spatial and temporal
correlations between the modelled and satellite-derived FNR vary over the
used satellite instruments. Brown-Steiner et al. (2015)
found persistent O3 biases under all configurations of a global
climate–chemistry model (GCCM) with detailed tropospheric chemistry.
Although FNR thresholds defined by Duncan et al. (2010) have been used
previously to investigate O3-NOx-VOC sensitivity in China
(Witte et al., 2011; Tang et al., 2012; Jin and Holloway, 2015), their
conclusions were based on the atmospheric situations in the United States
and may not be suitable for the more complicated air pollution in China,
concerning the different emission factors, sources, pollution levels, and
climatology. For example, compared with the United States, most cities in China
have higher aerosol levels (van Donkelaar et al., 2010; X. Li et al.,
2019). Secondary aerosol production may become a large sink of radicals,
which could shift O3 production toward a VOC-limited regime under these
FNR thresholds suited to the United States (Liu et al., 2012; K. Li et al.,
2019). It is therefore useful to describe surface O3 sensitivity using
FNR thresholds derived entirely from satellite-observed FNR and ground-based
measurements of O3. In addition, Schroeder et al. (2017)
using airborne measurements suggested that the range and span of FNR marking
the transitional regime varies regionally.
In this study, we assess whether space-based HCHO / NO2 ratios capture the
non-linearity of O3 chemistry by matching satellite observations with
ground-based O3 measurements over major Chinese cities. Thresholds
suited for China between space-based HCHO / NO2 and the ground-based
O3 response patterns are derived from observations instead of model
results. We focus on the spatial and temporal variability of O3
formation sensitivity using our FNR thresholds on a nationwide scale and in
typical cities from 2016 to 2019.
More recently, a new unique situation has occurred with the outbreak of the
COVID-19 pandemic, which provided a unique opportunity to demonstrate our
predicted effects on O3 pollution in China. Efforts to halt the spread
of COVID-19 have drastically reduced human activities worldwide
(Siciliano et al., 2020; H. Tian et al., 2020). As a result of these
restrictions, a significant reduction in primary air pollutant emissions,
especially in the concentration of NO2, has been noticed in China and
several European and American countries (Tobías et al., 2020; Wang
and Su, 2020; Bauwens et al., 2020; Ding et al., 2020). By contrast,
increasing O3 concentrations during the same period were observed in
densely populated metropolitan areas throughout the world (Siciliano et al., 2020;
Zoran et al., 2020; Huang et al., 2020).
Section 2 describes the data and methods used in this study. Section 3
presents our derived FNR thresholds method and variations of O3
formation sensitivity in China. In addition, impacts of the COVID-19
outbreak on O3 levels are discussed. Finally, Sect. 4 gives a brief
summary.
DataSatellite data
We use the NO2 and HCHO observations from the Ozone Monitoring
Instrument (OMI) aboard the National Aeronautics and Space Administration
(NASA) satellite Aura, which was launched in July 2004
(Levelt et al., 2006). In an ascending sun-synchronous
polar orbit, OMI passes the Equator at about 13:40 LT (local time),
providing global measurements of aerosol parameters, cloud, and various
trace gases (NO2 and HCHO among them) (Levelt et al.,
2006). The high spatial resolution (13 km × 24 km at nadir) allows
for observing fine details of atmospheric parameters (Jin and
Holloway, 2015). OMI data are considered to be reliable and of good quality
for the full mission thus far (Zara et
al., 2018). In addition, the OMI overpass time is well suited to detect the
O3 formation sensitivity during the afternoon, when O3
photochemical production peaks and when the boundary layer is high and the
solar zenith angle is small, maximizing instrument sensitivity to HCHO and
NO2 in the lower troposphere (Jin et al., 2017).
We use the OMI tropospheric NO2 and HCHO data products from the
European Quality Assurance for Essential Climate Variables project (QA4ECV,
http://www.qa4ecv.eu/, last access: 6 May 2021). NO2 data are compiled by the Royal
Netherlands Meteorological Institute (KNMI). The tropospheric NO2
column density is defined as the vertically integrated number of NO2
molecules between the Earth's surface and the tropopause per unit area. We
select QA4ECV NO2 daily observations following the recommendations
given in the product specification document (Boersma et al., 2011) for this
data product: (1) no processing error, (2) less than 10 % snow or ice
coverage, (3) solar zenith angle less than 80 ∘, and (4) cloud
radiance fraction less than 50 %. The QA4ECV NO2 monthly datasets are
processed with a spatial resolution of 0.125∘× 0.125∘. Boersma et
al. (2018) reported the single-pixel uncertainties for the QA4ECV NO2
columns are 35 %–45 % in the polluted regions; the monthly mean
NO2 columns are estimated to have an uncertainty of ±10 %.
The OMI tropospheric HCHO data are retrieved by the Belgian Institute for Space
Aeronomy (BIRA-IASB) (Smedt et al., 2017a). We select
processing_quality_flags = 0 or > 255, providing a selection of observations that is considered optimal.
Zara et al. (2018) found that the QA4ECV
HCHO slant column densities (SCDs) have uncertainties of 8–12×1015 molecule/cm2 and a remarkably stable trend (increase
< 1 %/yr). The QA4ECV HCHO monthly datasets are available
with a spatial resolution of 0.05∘× 0.05∘.
Temporal averaging has been shown to reduce the HCHO measurements
uncertainty and noise (Millet et al., 2008). We regrid the
monthly OMI HCHO data (0.05∘× 0.05∘) to the
same grid as for the monthly OMI NO2 data (0.125∘× 0.125∘).
NOx emission
Emission inventories of air pollutants are important sources of information
for policy makers and form essential input for air quality models. Bottom-up
inventories are usually compiled from statistics on emitting activities and
their typical emission factors but are sporadically updated
(Li et al., 2017). Satellite-derived
emission inventories have important advantages over bottom-up emission
inventories: they are spatially consistent, have high temporal resolution,
and provide up-to-date emission information (Mijling and van der A,
2012). In this study, we use monthly mean NOx surface emission
estimates derived from OMI observations of tropospheric NO2 columns
(the QA4ECV product discussed in Sect. 2) by the Daily Emission estimation
Constrained by Satellite Observations (DECSO) algorithm. Mijling
and van der A (2012) for the first time developed DECSO (version 1) by
calculating the sensitivity of concentration to emission based on a chemical
transport model and using trajectory analysis to account for transport away
from the source. Ding et al. (2015) improved DECSO
(version 3) and demonstrated that it is able to detect the monthly change of
NOx emissions due to air quality regulations on a city level. The
NOx emissions derived by the improved DECSO version 5 are in good
agreement with other bottom-up anthropogenic emission inventories. In
addition, the improved algorithm is able to better capture the seasonality
of NOx emissions. The precision of monthly NOx emissions derived
by DECSO version 5 for each grid cell is about 20 %
(Ding et al., 2017). Here, we use NOx emissions
derived by the latest DECSO version 5.1qa which provides monthly emissions
for the last decade (2007–2020) (Ding et al., 2018). These
datasets are available from
https://www.temis.nl/emissions/region_asia/datapage.php (last access: 6 May 2021).
Ground-based observations
Since 2012, the Chinese government at various levels began to establish a
national air quality monitoring network, which released real-time
ground-level O3 monitoring data to the public. By 2016, the
establishment of more than 1000 sites was completed, covering more
than 300 cities across the country. At each monitoring site, the
concentration of O3 is measured using the ultraviolet absorption
spectrometry method and differential optical absorption spectroscopy;
NO2 is measured using the chemiluminescence method by a set of
commercial instruments. The instrumental operation, maintenance, data
assurance, and quality control were conducted based on the most recent
revisions of China environmental protection standards (CMEE, 2013).
We use hourly O3 and NO2 concentrations (in standard conditions:
273 K, 101.325 kPa) from the network of ∼1000 sites operated
by the China Ministry of Ecology and Environment (CMEE) since 2016. CMEE
revised the monitoring of pollutants to a new reference conditions (298 K,
101.325 kPa) since 1 September 2018 (CMEE, 2018). Daily
ground-based O3 and NO2 observations are calculated from hourly
observations at OMI overpass time (average of 13:00 and 14:00 LT). In
this study, we convert the gas concentrations before 1 September 2018 from
the standard conditions to the reference conditions. The temperature
dependence is according to Charles's law (Eq. 1),
VstdTstd=VrefTref,
where Vstd is the volume of a gas under standard conditions, Vref
is the volume of a gas under reference conditions, Tstd (unit: K) is the
thermodynamic temperature of standard conditions, and Tref (unit: K) is the
thermodynamic temperature of reference conditions. The gas concentration
conversion follows
CstdCref=M/VstdM/Vref=VrefVstd,
where Cstd is the gas concentration under standard conditions, and Cref
is the gas concentration under reference conditions.
Because the Chinese national air quality monitoring network stations are
mostly located in the centre of cities or densely populated areas, which are usually
the most polluted regions, we select the Naha station, located on the small
island of Okinawa in Japan, as a location with a clean atmosphere. The hourly
O3 and NO2 observations of Naha station are provided by the
Japanese Atmospheric Environmental Regional Observation System (AEROS;
http://soramame.taiki.go.jp/Index.php, last access: 6 May 2021).
CLASS model
We simulate the non-linear relationship among O3, NO2, and HCHO
using the Chemistry Land-surface Atmosphere Soil Slab model (CLASS). We
performed a series of numerical experiments with the same dynamic and
chemistry conditions listed in Table 1, but we modified only the concentrations
of NO2 and HCHO. The initial mixing ratios of chemical species are
shown in Table S1 in the Supplement. The initial mixing ratio data are from
van Stratum et al. (2012). All
other species (except for molecular oxygen and nitrogen) are initialized at
zero, and we modified only the concentrations of NO2 and HCHO.
The CLASS model solves the diurnal evolution of dynamical variables
(temperature, specific humidity, and wind) and chemical species over time in
a well-mixed convective atmospheric boundary layer (ABL) in which
entrainment and boundary layer growth are considered (Vilà-Guerau de
Arellano et al., 2015; van Heerwaarden et al., 2010). All these variables
are assumed to be constant with height due to intense turbulent mixing
driven by convection (van Heerwaarden et al., 2010). The surface
is assumed to be homogeneous in this box model. Chemistry is represented by
a chemical scheme based on 27 reactions that control O3 formation
described by van Stratum et al. (2012), with O3, NOx, and isoprene as the most important species. This
simplified chemical scheme is able to represent the evolution of chemical
species in semirural areas (Janssen et al., 2012; van Stratum et al.,
2012). This chemical scheme is able to represent the evolution of
the O3–NOx–VOC–HOx cycle in semirural areas (Vilà-Guerau de
Arellano et al., 2011; Janssen et al., 2012; van Stratum et al., 2012). The
model has been validated under various dynamical conditions (Barbaro et
al., 2014; Janssen et al., 2012; van Heerwaarden et al., 2010).
Configuration and settings of the CLASS modelling system.
ItemStatus or valueTotal simulation time12 hTime step60 sInitial ABL height200 mMixed layerOnInitial mixed-layer potential temperature288 KInitial temperature jump at height1 KWindOffSurface scheme (sea or land)OffChemistryOnResultsO3 formation sensitivity regime classification
In Fig. 1a, the CLASS model is applied to generate O3 isopleths,
which illustrate O3 as a function of NO2 and HCHO values. The
isopleths show that O3 formation is a highly non-linear process in
relation to NO2 and HCHO. When NO2 is low, the O3 increases
with increasing NO2. As NO2 increases, the O3 eventually
reaches a local maximum. At higher NO2 concentrations, the O3
would decrease with increasing NO2.
(a) The simulated O3 isopleths versus NO2 and HCHO using
the CLASS model. (b) The 360 cities' monthly mean in situ O3 concentrations
versus in situ NO2 concentrations and HCHO columns from OMI observations in
the summer during 2016–2019. Note that daily ground-based O3 and NO2
observations are calculated from hourly observations at OMI overpass time
(averaged at 13:00 and 14:00 LT). The O3 numeric value of the grid
cells is average of all points falling in each bin. (c) Same as (b) but
with NO2 columns from OMI observations. (d) The top 10 % monthly
O3 values and corresponding FNRs of each city. FNR thresholds are
defined as the ±30 % range from the median of monthly O3 exceeding 160 µg/m3 in the top 10 % dataset.
We first evaluate if satellite-based HCHO and NO2 columns can capture
the non-linear O3–NO2–HCHO chemistry shown by the CLASS model. In
order to obtain a representative observation sample, we create monthly mean
ground-based O3 and NO2 observations of 360 cities across China
from the Chinese national air quality monitoring network from 2016 to 2019
and the background station observations from Naha, Japan, for comparison.
Temperature is also a major factor in O3 chemistry. O3 pollution
is rare when the ambient temperature is below 20 ∘C
(Sillman, 2003). The seasonality of ground-level O3
concentrations also exhibited monthly variability peaking in summer and
reaching the lowest levels in winter over China (W. N. Wang et al., 2017).
In addition, long NOx lifetime and low concentrations of OH and
RO2 radicals would lead most regions of China to a VOC-limited regime
in winter (Shah et al., 2020). Therefore, we focus
in this study on May–October as the summer period when meteorology is
favourable for O3 formation (Jin et al., 2017).
By directly connecting HCHO columns from OMI observations with ground-based
measurements of NO2 and O3 from 360 cities across China during May–October from 2016 to 2019 in Fig. 1b, we find that the satellite-based
HCHO columns and ground-based NO2 concentrations can capture non-linear
O3 chemistry consistent with the CLASS model results. It indicates that
tropospheric HCHO columns from OMI can represent the near-surface HCHO
environment as revealed by previous studies (Martin et al., 2004; Duncan
et al., 2010; Jin et al., 2017). The overall O3–NO2–HCHO chemistry
is also captured by satellite-based HCHO and NO2 columns in Fig. 1c,
where we construct the O3 isopleth using only observations.
Having established this relationship between satellite-based HCHO / NO2
columns and surface O3 concentrations, we subsequently derive the FNR
thresholds marking the O3 transitional regime. The local O3
maximum can be thought of as a dividing line separating two different
photochemical regimes (Sillman, 1999). According to the Chinese national
ambient air quality standards released in 2012, 1 h average O3
concentration should below 160 µg/m3 in rural regions and below
200 µg/m3 in urban regions (Li et al., 2018). We
assume that the monthly O3 concentration (daily O3 data are averaged at 13:00
and 14:00 LT) exceeding 160 µg/m3 has a large component that is
due to local photochemical production not meteorology or regional
transport. We calculated for each city the monthly mean surface O3 as
a function of the monthly column densities of NO2 and HCHO for all months
during May–October from 2016 to 2019. The results are shown in Fig. 1c.
We only consider observations of monthly HCHO columns higher than 2 × 1015 molecule/cm2 (detection limitation), NO2
columns more than 1.5 × 1015 molecule/cm2 (which are
defined as polluted regions), and O3 columns above 160 µg/m3
(minimizing the effect of background ozone). We then plot in Fig. 1d the
surface O3 concentrations as a function of the FNR to determine the range
of FNRs, which includes the O3 maximum for most (> 60 %)
cities. We define this range as the transition between the
NOx-limited and VOC-limited regimes.
It should be noted that the actual split between NOx-limited and
VOC-limited regimes includes a broad transitional region rather than a sharp
dividing line (Sillman, 1999). Although we reduce the noise by
gridding, there is a blurry transition between NOx-limited and
VOC-limited regimes. The lack of sharp and clear transitions between two
O3 sensitivity regimes is likely influenced by factors such as
meteorology, chemical and depositional loss of O3, and noisy satellite
data. We find a relationship between FNR and the O3 response patterns
that is qualitatively similar but quantitatively distinct across cities.
Taking into account the range of transitional regime, the FNR thresholds
[2.3, 4.2], marking the transitional regime, are defined as the ±30 % range from the median (3.28), covering the O3 maximum in most
(60 %) studied cities.
To minimize the effect of background O3 by transport or meteorological
variability, we use monthly mean O3 concentrations above 160 µg/m3 in summertime when the O3 chemistry is strongest. We
assume that the results are applicable for the whole of China. To check this
assumption, we investigate the FNR thresholds in different latitude zones
(18–28∘ N, 28–38∘ N, and
38–53∘ N) in Fig. S1 in the Supplement. Generally,
we conclude that the derived FNR thresholds range of [2.3, 4.2] for the
whole domain is a good representation for all latitude zones in China.
Figure S2a in the Supplement shows monthly O3 concentration in winter
(December–January–February), which rarely exceed 160 µg/m3, including the FNR
thresholds derived using summertime data. Based on Fig. S2b, we assume
that our FNR thresholds [2.3, 4.2] derived using summertime data will be
valid for all seasons. Three regimes can be roughly identified from the FNR
thresholds we adopted: a VOC-limited regime should occur when the FNR < 2.3, and a NOx-limited regime should occur when the FNR > 4.2. The FNR between 2.3 and 4.2 reflects the transition
between the two regimes.
Variations in O3 formation sensitivity in China
Figure 2a and b show the photochemical regime classification over China in
summer of 2016 and 2019 using our FNR thresholds. Combined with the China
provincial administrative division in Fig. S3 in the Supplement, we see
the VOC-limited regimes mainly appear in the North China Plain (NCP), the
Yangtze River Delta (YRD), and the Pearl River Delta (PRD), and the
NOx-limited regimes dominate the remaining areas, which are consistent
with results from N. Wang et al. (2019) and Jin and Holloway (2015). In the NCP, the VOC-limited regimes are found in Beijing and some
big cities in Hebei province, central regions in Shandong province, and Henan
province. Transitional regimes control the remaining regions of Shandong
province and Henan province and most regions of Hefei province. In the YRD,
the VOC-limited regimes are found in Shanghai and southern Jiangsu province.
In the PRD, the VOC-limited regimes are found in Guangzhou. Outside the NCP,
YRD and PRD, the VOC-limited regimes concentrate in city centres of
Shenyang, Chengdu, Chongqing, Xi'an, and Wuhan, which are surrounded by
transitional regimes in the suburban areas. It has been acknowledged that
the urban O3 formations are generally VOC-limited due to the large
amount of NOx emissions from diverse sectors, like transportation,
industry, residential sector, and power plants (Shao et al., 2009; Wang et
al., 2009; Sun et al., 2011). The NOx-limited or transitional regimes
dominated O3 formation in the suburban and rural areas of eastern China
(Xing et al., 2011; Jin et al., 2017).
(a) Photochemical regime classification over China in the summer
of 2016. (b) Same as (a) but for 2019. Note that no data grids in (a) and (b)
corresponds to monthly HCHO columns below the detection limit (2 × 1015 molecule/cm2) or NO2 columns lower than 1.5 × 1015 molecule/cm2. (c) Mean HCHO columns from OMI over China in
the summer of 2016. (d) Same as (c) but for 2019. (e) Mean NO2 columns
from OMI over China in the summer of 2016. (f) Same as (e) but for 2019.
Comparison of O3 sensitivities between 2016 and 2019 shows noticeable
changes from VOC-limited regime to transitional regime in the NCP, YRD, and
PRD. In the NCP, the continuous area of VOC-limited regimes that occurred in
2016 change to transitional regimes in 2019. The VOC-limited regimes remain
in central Beijing, Tianjin, Shijiazhuang, Jinan, and Zhengzhou. In the YRD,
Shanghai and Nanjing remain in the VOC-limited regime, and other cities mostly
change to the transitional regime. In the PRD, the VOC-limited regime still
controls Guangzhou, while the transitional regimes control its surrounding
cities.
Figure 2c and d show mean HCHO columns over China in the summer of 2016 and
2019. The columns exceed 15 × 1015 molecule/cm2 in
megacity clusters, such as in the NCP, YRD, and PRD, as well as the Sichuan Basin.
Shen et al. (2019) found large increases of HCHO columns during May–September over 2005–2016 in the NCP and the YRD, consistent with the
trend of anthropogenic VOC emissions. Our results show that the satellite
HCHO columns increase in the NCP and the YRD and decrease in the PRD and in
the Sichuan Basin during May–October of the 2016–2019 period. Figure 2e
shows mean NO2 columns over China in the summer of 2016. The NCP, YRD,
PRD, Sichuan Basin, and Urumqi have high levels (80 × 1015 molecule/cm2) of NO2 columns. Figure 2f shows the satellite
NO2 columns have a strong decline in the NCP, the PRD, Hunan, Hubei, and
Jiangxi provinces in summer from 2016 to 2019. However, the YRD shows
increasing NO2 columns in 2019.
We select typical cities (Beijing, Shanghai, Guangzhou, Neijiang, Lhasa, and
Naha) to analyse in more detail the O3 formation sensitivity in the
summers of 2016 to 2019 in Fig. 3. These cities are selected based on
their different chemical regimes in 2016. The locations of the six cities
are shown in Fig. S4 in the Supplement. Economically developed megacities
or provincial capital cities such as Beijing, Shanghai, and Guangzhou, with
high levels of tropospheric NO2 and HCHO, remain in the VOC-limited
regime over 2016–2019. The reduction of tropospheric NO2 results in a
shift in the O3 formation sensitivity in cities such as Neijiang over
2016–2019. Lhasa as a city with low NO2 and the background station in
Naha with even lower HCHO and NO2 columns remain in the
NOx-limited regime over 2016–2019.
The change of O3 formation sensitivity of six cities
(Beijing, Shanghai, Guangzhou, Neijiang, Lhasa and Naha) in summer from 2016
to 2019. The arrows represent time step from 2016 to 2019.
As we know, O3 increases with increasing NOx in the
NOx-limited regime and decreases with increasing NOx in the
VOC-limited regime. The contrast between NOx-limited and VOC-limited
regimes illustrates the difficulties involved in developing policies to
reduce O3 in NOx polluted regions. Reductions in VOCs will only be
effective in reducing O3 if VOC-limited chemistry predominates.
Reductions in NOx will be effective only if NOx-limited chemistry
predominates and may actually increase O3 in VOC-sensitive regions. If
cities belonging to the VOC-limited regime like Beijing only focus on the
reduction of NOx while ignore the control of VOC emissions, they will
experience a process of rising O3 concentrations, the more NOx
decrease, the greater the increase in O3 will be.
Observed response of ground-level O3 to chemical formation
sensitivity
To validate the regimes derived from satellite observations, we also analyse
the surface NO2 observations from ground-based measurements. Figure 4a
and b show the mean ground-based NO2 concentrations in summer of 2016
and 2019. According to the NOx surface emission estimates derived with
DECSO from OMI observations, the NOx emissions in eastern China
(18∘ N, 104∘ E, 41.5∘ N, 124∘ E)
decrease from 5.93 Tg/yr in 2016 to 4.21 Tg/yr in 2019. Such a strong
decline in NOx emissions led to decreasing ambient NO2
concentrations at NCP (Beijing, Shijiazhuang, Zhengzhou, Jinan) and YRD
(Hefei and other cities in Anhui province). In Fig. 4c, the national
average NO2 concentration decrease by 14.4 % in summer from 2016 to
2019.
(a) Mean ground-based NO2 concentration at each city in the
summer of 2016. (b) Same as (a) but for 2019. (c) The bars indicate the
number of cities (left axis) in a certain NO2 range in summer from 2016
to 2019. The black line indicates the average NO2 concentration (right
axis) of all cities. (d) Mean ground-based O3 concentration at each
city in summer of 2016. (e) Same as (d) but for 2019. (f) Same as (c) but
for O3. Note that daily in situ NO2 and O3 data are the average of
13:00–14:00 LT of the sites in each city.
Figure 4d and e show the mean ground-based O3 concentration of about
360 cities across China in summer of 2016 and 2019. Generally, the O3
levels in western China are lower than in eastern China. In 2016, few cities
have an average O3 concentration above 140 µg/m3. In 2019,
cities with a mean O3 concentration exceeding 140 µg/m3 occurred at the NCP (Tianjin, Shijiazhuang, some cities in Shandong and
Henan province), the YRD (Nanjing), and the PRD (Guangzhou). In Fig. 4f,
we see the number of cities with average O3 values above 140 µg/m3 increases rapidly from 2.20 % in 2016 to 31.37 % in 2019. The
cities with an average O3 value below 80 µg/m3 decrease from
11.02 % in 2016 to 2.24 % in 2019. In addition, the nationwide O3
average in summer increases year by year from 2016 (104.86 µg/m3)
to 2019 (125.14 µg/m3). K. Li et al. (2019) reported the
increasing O3 trends in summer in megacity clusters of eastern China
and the highest O3 concentrations are in the NCP, which are consistent
with our results.
A complex coupling of primary emissions, chemical transformation, and
dynamic transport at different scales determine the O3 pollution
(Jacob, 1999). NOx and VOCs play important roles in O3
formation. Emissions of NOx and VOCs to the environment are the
starting point of O3 pollution problems. During the past decade in
China, ambitious steps have been taken to control NOx emissions. In
2013, the Chinese State Council issued the APPAP. Stringent control measures
were carried out since then, including phasing out highly emitting industries,
closing outdated factories, tightening industrial emission standard,
improving fuel quality (N. Wang et al., 2019). However, to the other
important O3 precursors, VOCs, less attention has been given in
emission control strategy. M. Li et al. (2019)
concluded that anthropogenic NMVOC emissions in China during 1990–2017 have
been increasing continuously due to the dramatic growth in activity rates
and absence of effective control measures. Following China's past control
strategy on VOCs, we can regard VOC emissions as rising or in steady state.
(a) Differences in total NOx emissions derived from OMI
observations in summer in east China between 2019 and 2016. (b) Variations
in total NOx emissions in five cities (Beijing, Shanghai, Guangzhou,
Neijiang, and Naha) in summer from 2016 to 2019. (c) Variations in mean
ground-based O3 concentrations in five cities in summer from 2016 to
2019.
The reduction of the NOx emissions for cities in the VOC-limited regime
is one of the main reason for the increasing of O3. Figure 5a shows the
difference of total NOx emissions derived from OMI observations in
summer in east China between 2019 and 2016. A decline in NOx emissions
centres at the NCP, YRD and PRD, where most areas belong to the VOC-limited
regime. In order to provide further insight into the impact of NOx
emission variations on O3 concentrations, five selected typical cities
(Beijing, Shanghai, Guangzhou, Neijiang and Naha) are shown in more detail
(see Fig. 5b and c). For cities under the control of VOC-limited
chemistry (Beijing, Shanghai and Guangzhou), accompanied with decreasing
NOx emissions, O3 concentrations generally show an opposite
behaviour to NOx emissions. The O3 formation sensitivity in
Neijiang shows a shift from the transitional to the NOx-limited regime
over 2016–2019. The reduction of NOx emissions in the transitional
regime is accompanied by decreasing O3 in Neijiang. Although the
O3 data in Naha for 2016–2018 are unavailable, we see that O3
concentrations in Naha are low in 2019, and NOx emissions are stable
during 2016–2019. Note that we find a qualitative relationship between
NOx emission and the O3 response patterns, confirming the non-linear
O3–NO2–VOC chemistry but not in a quantitative sense. For example, the
changes of NOx emissions in Beijing (-2.17 Gg N/cell), Shanghai (-1.18 Gg N/cell), Guangzhou (-0.28 Gg N/cell), and Neijiang (-0.15 Gg N/cell)
during 2016–2019 lead to different levels of O3 changes in Beijing
(10.43 µg/m3), Shanghai (7.81 µg/m3), Guangzhou (25.54 µg/m3), and Neijiang (-22.66µg/m3). Because of the
VOC-limited chemistry conditions, O3 increases with decreasing
NOx emissions in Beijing, Shanghai, and Guangzhou. The NOx-limited
conditions lead to decreasing O3 with decreasing NOx emissions
in Neijiang. Compared with Beijing, NOx emissions in Guangzhou remained
basically constant in 2016 and 2019. But O3 concentrations in
Guangzhou increased more than in Beijing. The local O3 formation
sensitivity is helpful to present the way that O3 responds to NOx
emission, but VOC emission are needed when discussing their relationship in
a quantitative way.
Enhanced O3 levels during the COVID-19 lockdown in China
The measures in response to the outbreak of the COVID-19 lead to sudden
changes of NOx emissions and anthropogenic HCHO emissions in China in
the beginning of 2020 (Wang et al., 2020; Hui et al., 2020). We analyse
the change of O3 concentrations during the lockdown period to validate
our method. To look into COVID-19 lockdown impacts on short-term O3
level, we choose two time periods covering 357 cities across China: period I
(3–23 January 2020) and period II (9–29 February 2020), to avoid the
coincidence of Chinese New Year holidays (24 January to 8 February 2020).
(a) Differences in mean ground-based O3 concentrations in
east China between period I and period II. (b) Differences in mean NOx
emissions in east China between period I and period II. (c) O3
formation sensitivity in east China during period I. (d) Same as (c), but
for period II. Note that period I (3–23 January 2020) is before the lockdown,
and period II (9–29 February 2020) is during the lockdown.
Figure 6a shows enhanced O3 levels in most cities of eastern China
during the COVID-19 lockdown, except for some cities in PRD and Fujian
province. The cities with O3 concentration increases of more than 40 µg/m3 are located in the NCP and the YRD, i.e. the populous regions of
China, indicating a potential negative health effect from O3 exposure
in these regions. Figure 6b shows strong reductions in NOx emissions in
eastern China, especially in Henan, Hubei, and Jiangsu provinces, where as a
consequence of the lockdown, transportation, construction, and light
industry activities have been dramatically decreased.
Assuming that our observation-based FNR thresholds derived using summertime
data also apply during winter, we see that most regions of eastern China
belong to the VOC-limited regime during periods I and II in Fig. 6c and d.
Previous studies also reported that the O3 chemistry in the urban areas
in China in wintertime is in a VOC-limited regime due to the relative lack
of HOx radicals (Seinfeld and Pandis, 2016). During winter
(VOC-limited conditions), when the concentration of NOx is high and
the level of UV radiation is low, the O3 production varies inversely
with the NOx concentration (Sillman et al., 1990). During the
lockdown period, both the anthropogenic emissions of NOx and VOCs were
reduced. The NOx reduction during the lockdown is higher than the VOC
reduction according to Sicard et al. (2020). The reductions of VOC emissions
are generally effective in reducing O3 concentrations. However, such
air quality improvements are largely offset by reductions in NOx
emissions leading to increases in O3 concentrations due to the strongly
VOC-limited conditions in the NCP in winter (Xing et al., 2020). The
NOx reduction during the lockdown is higher than the VOC reduction
(Sicard et al., 2020). Thus, a reduction in NOx leads to an
increase in the O3 concentrations in most regions of eastern China
during period II. Besides, reduction of freshly emitted NO in particular
from road traffic alleviates O3 titration locally (Seinfeld and
Pandis, 2016; Levy et al., 2014). The O3 titration occurs particularly
in winter (less photolysis reactions of NO2) under high NOx levels
(Sillman, 1999). However, the lockdown measures result primarily in
a lower titration of O3 by NO due to the reduction in local NOx
emissions by road transport, which also enhances O3 levels in urban
areas. On the other hand, some cities, mainly located in southeastern China,
showed decreasing O3 levels. Zhao et al. (2020) concluded
that the cause of O3 decline in these cities is the emission changes of
NOx and VOC. In Fig. 6c we see that some cities in Fujian and
Guangdong provinces belong to the transitional regime. Theoretically, the
transitional regime should correspond to the conditions at which O3
formation is most efficient, indicating that reductions or increases in
NOx and VOCs will reduce the O3 concentration.
Conclusion
Satellite-based HCHO / NO2 ratios and ground-based O3 measurements
were directly connected to capture the non-linearity of surface O3
chemistry over major Chinese cities in this study. Evaluating the FNR
thresholds marking the O3 transitional regime in which O3
formation is less sensitive to the precursors, we found a broad transitional
region, which reflects differences in factors among 360 cities, such as
emissions, meteorology, and regional transport. The national FNR thresholds
are defined as follows: a VOC-limited regime should occur for FNR < 2.3 and a NOx-limited regime should occur for FNR > 4.2. The
FNR between 2.3 and 4.2 reflects the transition between the two regimes. Our
FNR thresholds derived from satellite and ground-based observations are
higher than previously reported model-based values. The non-linear chemistry of
O3 depends on its precursors NO2 and VOCs with contributions from
both local and regional sources (Xue
et al., 2014). Modelling studies are good at simulating the response of
surface O3 to an overall reduction in NOx or VOC emissions. The
FNR thresholds derived with in situ O3 observations will be more indicative of
the local O3 chemistry than the model, including the effect of NOx
titration over urban areas (Jin et al., 2020).
We analysed the spatial and temporal variability of O3 formation
sensitivity using our FNR thresholds over China from 2016 to 2019. Our
results showed that O3 formation sensitivity tends to be VOC-limited
over urban areas and NOx-limited over rural and remote areas in China.
In 2016, the VOC-limited regimes mainly appear in the NCP, the YRD, and the
PRD. In 2019, there was a shift in most NCP regions from the VOC-limited to
the transitional regime. The area with a VOC-limited regime in the YRD and
PRD also shrank. We found that O3 formation sensitivity changes in
these regions were associated with a strong decline in tropospheric NO2
columns in the NCP and the PRD. For megacities such as Beijing and
Guangzhou, although they remained in the VOC-limited regime over 2016–2019,
there was still a decrease in NO2 columns. Consistent with decreasing
tropospheric NO2 columns, the national average surface NO2
concentration decreased by 14.4 % in summer from 2016 to 2019 and the
NOx emissions in eastern China decreased from 5.93 Tg/yr in 2016 to
4.21 Tg/yr in 2019. This detected spatial expansion of the transitional
regime and NOx emission reduction in the VOC-limited regime has contributed
to rising surface O3 concentrations. The nationwide averaged O3
concentration in summer increased year by year from 2016 (104.86 µg/m3) to 2019 (125.14 µg/m3). The cities with average
O3 values above 140 µg/m3 increased rapidly from 2.20 % in
2016 to 31.37 % in 2019.
Satellite instruments measure the vertically integrated column density,
which we use as a proxy of the actual surface concentrations. To reduce the
effect of short-term variability in vertical distributions caused by
meteorological changes, we use monthly mean averages. Therefore, our
satellite-based HCHO / NO2 method is limited to identification of
long-term evolution in O3 sensitivity, focusing on understanding the
average air quality.
We presented the level of O3 formed from photo-oxidation of total
measured HCHO only not differentiating the contributions from different
sources (directly emitted or photochemically formed). Due to the higher
temperature and stronger solar radiation in summer, the higher concentration
level of HCHO mainly results from the intense photo-oxidation of VOCs.
Emission sources of HCHO, as a tracer of VOCs, can be anthropogenic and
biogenic. Shen et al. (2019) found that the OMI HCHO distribution
follows their anthropogenic inventory in megacity clusters over China, while
it does not follow the biogenic emissions inventory. Despite the fact that
local sources of anthropogenic VOCs are difficult to identify, our FNR
thresholds derived from satellite-based information have the potential to
provide important information to air quality planners. Compared with
stringent control measures for NOx emissions, VOC emissions got less
attention as the other O3 precursor in China. The case study of O3
level changes during the COVID-19 lockdown in China demonstrated that the
strong reductions in anthropogenic NOx emissions resulted in
significant O3 enhancement due to the VOC-limited regime in winter. It
indicates that a protocol with strict measures to control NOx
emissions,without simultaneous VOC emissions controls for power plants and
heavy industry, such as petrochemical facilities, achieves only limited
effects on O3 pollution.
Data availability
Satellite data used in this research can be obtained from public sources.
The OMI tropospheric NO2 product from the QA4ECV project can be
obtained from 10.21944/qa4ecv-no2-omi-v1.1 (Boersma et al., 2017), and the
HCHO product can be obtained from 10.18758/71021031 (De Smedt et al., 2017b).
The monthly mean NOx emission products derived from OMI observations by
DECSO v5.1qa can be obtained from
https://www.temis.nl/emissions/region_asia/datapage.php (Ding et al., 2018).
The hourly O3 and NO2 observations of Chinese ground stations can
be accessed from third parties (http://www.pm25.in, China National Environmental Monitoring Center, 2021a, http://www.aqicn.org, China National Environmental Monitoring Center, 2021b).
The hourly O3 and NO2 observations of Naha station are provided by
the Japanese Atmospheric Environmental Regional Observation System (AEROS;
http://soramame.taiki.go.jp/DownLoad.php, Japanese Ministry of the Environment, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-7253-2021-supplement.
Author contributions
WW and RvdA provided satellite data, tools and analysis. RvdA, JD, MvW and TC
undertook the conceptualization and investigation. WW prepared the original
draft. RvdA and JD carried out the review and editing. All authors discussed the
results and commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Acknowledgements
The support provided by China Scholarship Council (CSC) during a visit by
Wannan Wang to Royal Netherlands Meteorological Institute (KNMI) is
acknowledged.
Review statement
This paper was edited by Tim Butler and reviewed by two anonymous referees.
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